Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

Discrete random probability measures and the exchangeable random partitions they induce
are key tools for addressing a variety of estimation and prediction problems in Bayesian …

Bayesian nonparametric inference is a relatively young area of research and it has recently
undergone a strong development. Most of its success can be explained by the considerable …

This paper concerns the use of Markov chain Monte Carlo methods for posterior sampling in
Bayesian nonparametric mixture models with normalized random measure priors. Making …

Many data sets can be represented as a sequence of interactions between entities--for
example communications between individuals in a social network, protein-protein …

A review of uncertainty quantification for density estimation Page 1 Statistics Surveys Vol. 15
(2021) 1–71 ISSN: 1935-7516 https://doi.org/10.1214/21-SS130 A review of uncertainty …

Dirichlet process mixtures are particularly sensitive to the value of the precision parameter
controlling the behavior of the latent partition. Randomization of the precision through a prior …

Statistical methods for network data often parameterize the edge-probability by attributing
latent traits such as block structure to the vertices and assume exchangeability in the sense …

Bayesian nonparametric models offer a flexible and powerful framework for statistical model
selection, enabling the adaptation of model complexity to the intricacies of diverse datasets …

We review the class of species sampling models (SSM). In particular, we investigate the
relation between the exchangeable partition probability function (EPPF) and the predictive …